WareVision: CNN Barcode Detection-Based UAV Trajectory Optimization for Autonomous Warehouse Stocktaking

This letter presents a heterogeneous Unmanned Aerial Vehicle (UAV)-based robotic system for real-time barcode detection and scanning using Convolutional Neural Networks (CNN). The proposed approach improves the UAV's localization using scanned barcodes as landmarks in a real warehouse with low-...

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Bibliographic Details
Published in:IEEE robotics and automation letters 2020-10, Vol.5 (4), p.6647-6653
Main Authors: Kalinov, Ivan, Petrovsky, Alexander, Ilin, Valeriy, Pristanskiy, Egor, Kurenkov, Mikhail, Ramzhaev, Vladimir, Idrisov, Ildar, Tsetserukou, Dzmitry
Format: Article
Language:English
Subjects:
AI-based methods
Artificial neural networks
Bar codes
Cameras
computer vision for automation
Drones
Inventory management
Meters
multi-robot systems
object detection
Pallets
Real-time systems
Robot kinematics
Scanning
segmentation and categorization
Trajectory optimization
Unmanned aerial vehicles
Warehouses
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Summary:This letter presents a heterogeneous Unmanned Aerial Vehicle (UAV)-based robotic system for real-time barcode detection and scanning using Convolutional Neural Networks (CNN). The proposed approach improves the UAV's localization using scanned barcodes as landmarks in a real warehouse with low-light conditions. Instead of using the standard overlapping snake-based grid (OSBG) trajectory, we implement a novel approach for flight-path optimization based on barcode locations. This approach reduces the time of warehouse stocktaking and decreases the number of mistakes in barcode scanning.
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3010733